Overview

Dataset statistics

Number of variables19
Number of observations6795
Missing cells2876
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1008.8 KiB
Average record size in memory152.0 B

Variable types

Categorical5
Numeric14

Alerts

age is highly overall correlated with ghHigh correlation
wt is highly overall correlated with bmi and 4 other fieldsHigh correlation
ht is highly overall correlated with leg and 2 other fieldsHigh correlation
bmi is highly overall correlated with wt and 4 other fieldsHigh correlation
leg is highly overall correlated with ht and 1 other fieldsHigh correlation
arml is highly overall correlated with wt and 3 other fieldsHigh correlation
armc is highly overall correlated with wt and 3 other fieldsHigh correlation
waist is highly overall correlated with wt and 3 other fieldsHigh correlation
tri is highly overall correlated with bmi and 2 other fieldsHigh correlation
sub is highly overall correlated with wt and 4 other fieldsHigh correlation
gh is highly overall correlated with age and 2 other fieldsHigh correlation
sex is highly overall correlated with ht and 2 other fieldsHigh correlation
tx is highly overall correlated with gh and 1 other fieldsHigh correlation
dx is highly overall correlated with gh and 1 other fieldsHigh correlation
tx is highly imbalanced (55.7%)Imbalance
income has 320 (4.7%) missing valuesMissing
leg has 231 (3.4%) missing valuesMissing
arml has 179 (2.6%) missing valuesMissing
armc has 188 (2.8%) missing valuesMissing
waist has 239 (3.5%) missing valuesMissing
tri has 481 (7.1%) missing valuesMissing
sub has 971 (14.3%) missing valuesMissing
albumin has 89 (1.3%) missing valuesMissing
bun has 89 (1.3%) missing valuesMissing
SCr has 89 (1.3%) missing valuesMissing

Reproduction

Analysis started2024-05-18 08:46:05.542842
Analysis finished2024-05-18 08:46:31.791080
Duration26.25 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.2 KiB
female
3423 
male
3372 

Length

Max length6
Median length6
Mean length5.0075055
Min length4

Characters and Unicode

Total characters34026
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
female 3423
50.4%
male 3372
49.6%

Length

2024-05-18T16:46:31.878141image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T16:46:32.016675image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
female 3423
50.4%
male 3372
49.6%

Most occurring characters

ValueCountFrequency (%)
e 10218
30.0%
m 6795
20.0%
a 6795
20.0%
l 6795
20.0%
f 3423
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34026
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10218
30.0%
m 6795
20.0%
a 6795
20.0%
l 6795
20.0%
f 3423
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 34026
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10218
30.0%
m 6795
20.0%
a 6795
20.0%
l 6795
20.0%
f 3423
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34026
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10218
30.0%
m 6795
20.0%
a 6795
20.0%
l 6795
20.0%
f 3423
 
10.1%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct813
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.2857
Minimum12
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:32.137288image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile14.333333
Q125.666667
median43.75
Q361.333333
95-th percentile80
Maximum80
Range68
Interquartile range (IQR)35.666667

Descriptive statistics

Standard deviation20.594593
Coefficient of variation (CV)0.46503934
Kurtosis-1.1732763
Mean44.2857
Median Absolute Deviation (MAD)17.75
Skewness0.13173059
Sum300921.33
Variance424.13725
MonotonicityNot monotonic
2024-05-18T16:46:32.275238image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 364
 
5.4%
17.08333333 23
 
0.3%
17.66666667 20
 
0.3%
14.08333333 19
 
0.3%
16.5 19
 
0.3%
19.5 19
 
0.3%
14.16666667 18
 
0.3%
17.83333333 18
 
0.3%
16.16666667 18
 
0.3%
14 18
 
0.3%
Other values (803) 6259
92.1%
ValueCountFrequency (%)
12 6
 
0.1%
12.08333333 10
0.1%
12.16666667 10
0.1%
12.25 7
0.1%
12.33333333 16
0.2%
12.41666667 9
0.1%
12.5 15
0.2%
12.58333333 15
0.2%
12.66666667 15
0.2%
12.75 10
0.1%
ValueCountFrequency (%)
80 364
5.4%
79.91666667 1
 
< 0.1%
79.83333333 3
 
< 0.1%
79.75 2
 
< 0.1%
79.66666667 6
 
0.1%
79.58333333 1
 
< 0.1%
79.5 3
 
< 0.1%
79.41666667 4
 
0.1%
79.33333333 3
 
< 0.1%
79.25 2
 
< 0.1%

re
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size53.2 KiB
Non-Hispanic White
3117 
Mexican American
1366 
Non-Hispanic Black
1217 
Other Hispanic
706 
Other Race Including Multi-Racial
389 

Length

Max length33
Median length18
Mean length18.04106
Min length14

Characters and Unicode

Total characters122589
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-Hispanic White
2nd rowNon-Hispanic Black
3rd rowNon-Hispanic Black
4th rowMexican American
5th rowNon-Hispanic White

Common Values

ValueCountFrequency (%)
Non-Hispanic White 3117
45.9%
Mexican American 1366
20.1%
Non-Hispanic Black 1217
 
17.9%
Other Hispanic 706
 
10.4%
Other Race Including Multi-Racial 389
 
5.7%

Length

2024-05-18T16:46:32.404983image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T16:46:32.541009image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
non-hispanic 4334
30.2%
white 3117
21.7%
mexican 1366
 
9.5%
american 1366
 
9.5%
black 1217
 
8.5%
other 1095
 
7.6%
hispanic 706
 
4.9%
race 389
 
2.7%
including 389
 
2.7%
multi-racial 389
 
2.7%

Most occurring characters

ValueCountFrequency (%)
i 17096
13.9%
n 12884
 
10.5%
a 10156
 
8.3%
c 10156
 
8.3%
7573
 
6.2%
e 7333
 
6.0%
H 5040
 
4.1%
s 5040
 
4.1%
p 5040
 
4.1%
- 4723
 
3.9%
Other values (19) 37548
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91202
74.4%
Uppercase Letter 19091
 
15.6%
Space Separator 7573
 
6.2%
Dash Punctuation 4723
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 17096
18.7%
n 12884
14.1%
a 10156
11.1%
c 10156
11.1%
e 7333
8.0%
s 5040
 
5.5%
p 5040
 
5.5%
t 4601
 
5.0%
o 4334
 
4.8%
h 4212
 
4.6%
Other values (8) 10350
11.3%
Uppercase Letter
ValueCountFrequency (%)
H 5040
26.4%
N 4334
22.7%
W 3117
16.3%
M 1755
 
9.2%
A 1366
 
7.2%
B 1217
 
6.4%
O 1095
 
5.7%
R 778
 
4.1%
I 389
 
2.0%
Space Separator
ValueCountFrequency (%)
7573
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4723
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 110293
90.0%
Common 12296
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 17096
15.5%
n 12884
11.7%
a 10156
 
9.2%
c 10156
 
9.2%
e 7333
 
6.6%
H 5040
 
4.6%
s 5040
 
4.6%
p 5040
 
4.6%
t 4601
 
4.2%
o 4334
 
3.9%
Other values (17) 28613
25.9%
Common
ValueCountFrequency (%)
7573
61.6%
- 4723
38.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 17096
13.9%
n 12884
 
10.5%
a 10156
 
8.3%
c 10156
 
8.3%
7573
 
6.2%
e 7333
 
6.0%
H 5040
 
4.1%
s 5040
 
4.1%
p 5040
 
4.1%
- 4723
 
3.9%
Other values (19) 37548
30.6%

income
Categorical

MISSING 

Distinct14
Distinct (%)0.2%
Missing320
Missing (%)4.7%
Memory size53.2 KiB
>= 100000
877 
[25000,35000)
845 
[35000,45000)
610 
[75000,100000)
564 
[20000,25000)
563 
Other values (9)
3016 

Length

Max length14
Median length13
Mean length12.030734
Min length7

Characters and Unicode

Total characters77899
Distinct characters15
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[25000,35000)
2nd row[45000,55000)
3rd row[10000,15000)
4th row[25000,35000)
5th row[35000,45000)

Common Values

ValueCountFrequency (%)
>= 100000 877
12.9%
[25000,35000) 845
12.4%
[35000,45000) 610
9.0%
[75000,100000) 564
8.3%
[20000,25000) 563
8.3%
[10000,15000) 531
7.8%
[45000,55000) 522
7.7%
[15000,20000) 456
6.7%
[55000,65000) 376
5.5%
[5000,10000) 315
 
4.6%
Other values (4) 816
12.0%
(Missing) 320
 
4.7%

Length

2024-05-18T16:46:32.669433image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1184
15.5%
100000 877
11.5%
25000,35000 845
11.0%
35000,45000 610
8.0%
75000,100000 564
7.4%
20000,25000 563
7.4%
10000,15000 531
6.9%
45000,55000 522
6.8%
15000,20000 456
 
6.0%
55000,65000 376
 
4.9%
Other values (4) 1131
14.8%

Most occurring characters

ValueCountFrequency (%)
0 39882
51.2%
5 8816
 
11.3%
[ 5291
 
6.8%
, 5291
 
6.8%
) 5291
 
6.8%
1 3274
 
4.2%
2 2734
 
3.5%
3 1455
 
1.9%
1184
 
1.5%
4 1132
 
1.5%
Other values (5) 3549
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58781
75.5%
Open Punctuation 5291
 
6.8%
Other Punctuation 5291
 
6.8%
Close Punctuation 5291
 
6.8%
Math Symbol 2061
 
2.6%
Space Separator 1184
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39882
67.8%
5 8816
 
15.0%
1 3274
 
5.6%
2 2734
 
4.7%
3 1455
 
2.5%
4 1132
 
1.9%
7 838
 
1.4%
6 650
 
1.1%
Math Symbol
ValueCountFrequency (%)
> 1109
53.8%
= 877
42.6%
< 75
 
3.6%
Open Punctuation
ValueCountFrequency (%)
[ 5291
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5291
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5291
100.0%
Space Separator
ValueCountFrequency (%)
1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39882
51.2%
5 8816
 
11.3%
[ 5291
 
6.8%
, 5291
 
6.8%
) 5291
 
6.8%
1 3274
 
4.2%
2 2734
 
3.5%
3 1455
 
1.9%
1184
 
1.5%
4 1132
 
1.5%
Other values (5) 3549
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39882
51.2%
5 8816
 
11.3%
[ 5291
 
6.8%
, 5291
 
6.8%
) 5291
 
6.8%
1 3274
 
4.2%
2 2734
 
3.5%
3 1455
 
1.9%
1184
 
1.5%
4 1132
 
1.5%
Other values (5) 3549
 
4.6%

tx
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.2 KiB
0
6171 
1
624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6795
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6171
90.8%
1 624
 
9.2%

Length

2024-05-18T16:46:32.775955image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T16:46:32.879208image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6171
90.8%
1 624
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 6171
90.8%
1 624
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6795
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6171
90.8%
1 624
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 6795
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6171
90.8%
1 624
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6171
90.8%
1 624
 
9.2%

dx
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.2 KiB
0
5881 
1
914 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6795
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5881
86.5%
1 914
 
13.5%

Length

2024-05-18T16:46:32.970160image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T16:46:33.098049image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5881
86.5%
1 914
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 5881
86.5%
1 914
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6795
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5881
86.5%
1 914
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common 6795
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5881
86.5%
1 914
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5881
86.5%
1 914
 
13.5%

wt
Real number (ℝ)

HIGH CORRELATION 

Distinct1022
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.370625
Minimum28
Maximum239.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:33.218085image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile49.97
Q164
median76.3
Q391.1
95-th percentile119
Maximum239.4
Range211.4
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation21.930903
Coefficient of variation (CV)0.27631007
Kurtosis2.7069423
Mean79.370625
Median Absolute Deviation (MAD)13.3
Skewness1.1008427
Sum539323.4
Variance480.96452
MonotonicityNot monotonic
2024-05-18T16:46:33.345738image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.1 22
 
0.3%
61.2 21
 
0.3%
70.3 21
 
0.3%
65.7 20
 
0.3%
66.6 20
 
0.3%
70.8 20
 
0.3%
77.7 20
 
0.3%
69.8 20
 
0.3%
74.9 20
 
0.3%
62.6 19
 
0.3%
Other values (1012) 6592
97.0%
ValueCountFrequency (%)
28 1
< 0.1%
29.1 1
< 0.1%
30 1
< 0.1%
32.2 1
< 0.1%
33 1
< 0.1%
33.2 1
< 0.1%
33.9 2
< 0.1%
34.5 1
< 0.1%
34.6 1
< 0.1%
34.8 1
< 0.1%
ValueCountFrequency (%)
239.4 1
< 0.1%
230.7 1
< 0.1%
223 1
< 0.1%
203 1
< 0.1%
201 1
< 0.1%
196.6 1
< 0.1%
195.3 1
< 0.1%
188.9 1
< 0.1%
186.9 1
< 0.1%
184.7 1
< 0.1%

ht
Real number (ℝ)

HIGH CORRELATION 

Distinct532
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.04296
Minimum123.3
Maximum202.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:33.481781image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum123.3
5-th percentile150.8
Q1159.6
median166.6
Q3174.5
95-th percentile184.2
Maximum202.7
Range79.4
Interquartile range (IQR)14.9

Descriptive statistics

Standard deviation10.264984
Coefficient of variation (CV)0.06145116
Kurtosis-0.3492805
Mean167.04296
Median Absolute Deviation (MAD)7.4
Skewness0.10555254
Sum1135056.9
Variance105.36989
MonotonicityNot monotonic
2024-05-18T16:46:33.624747image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.6 41
 
0.6%
162.8 38
 
0.6%
161 35
 
0.5%
161.2 34
 
0.5%
168.3 34
 
0.5%
175 33
 
0.5%
163.7 33
 
0.5%
166.5 32
 
0.5%
165.8 31
 
0.5%
163.4 31
 
0.5%
Other values (522) 6453
95.0%
ValueCountFrequency (%)
123.3 1
< 0.1%
135.4 1
< 0.1%
135.7 1
< 0.1%
137.5 1
< 0.1%
138.9 1
< 0.1%
139.4 1
< 0.1%
139.8 2
< 0.1%
139.9 1
< 0.1%
140 1
< 0.1%
140.3 1
< 0.1%
ValueCountFrequency (%)
202.7 1
< 0.1%
201.7 1
< 0.1%
199.6 1
< 0.1%
199.3 1
< 0.1%
199.2 1
< 0.1%
198.2 1
< 0.1%
197.5 1
< 0.1%
197.4 1
< 0.1%
196.6 1
< 0.1%
195.9 1
< 0.1%

bmi
Real number (ℝ)

HIGH CORRELATION 

Distinct2389
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.321741
Minimum13.18
Maximum84.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:33.763967image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum13.18
5-th percentile19.15
Q123.43
median27.29
Q331.88
95-th percentile41
Maximum84.87
Range71.69
Interquartile range (IQR)8.45

Descriptive statistics

Standard deviation6.9501104
Coefficient of variation (CV)0.24539842
Kurtosis3.4233109
Mean28.321741
Median Absolute Deviation (MAD)4.16
Skewness1.230485
Sum192446.23
Variance48.304035
MonotonicityNot monotonic
2024-05-18T16:46:33.893171image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.08 12
 
0.2%
23.81 11
 
0.2%
26.31 11
 
0.2%
28.67 11
 
0.2%
23.3 10
 
0.1%
25.36 10
 
0.1%
24.12 10
 
0.1%
27.2 10
 
0.1%
31.64 10
 
0.1%
28.47 10
 
0.1%
Other values (2379) 6690
98.5%
ValueCountFrequency (%)
13.18 1
< 0.1%
13.3 1
< 0.1%
14.39 1
< 0.1%
14.55 1
< 0.1%
14.59 1
< 0.1%
14.75 1
< 0.1%
14.88 1
< 0.1%
14.97 1
< 0.1%
15.02 1
< 0.1%
15.17 1
< 0.1%
ValueCountFrequency (%)
84.87 1
< 0.1%
81.25 1
< 0.1%
71.3 1
< 0.1%
68.63 1
< 0.1%
67.83 1
< 0.1%
67.71 1
< 0.1%
66.96 1
< 0.1%
66.32 1
< 0.1%
65.62 1
< 0.1%
65.19 1
< 0.1%

leg
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct232
Distinct (%)3.5%
Missing231
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean38.409324
Minimum20.4
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:34.035629image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum20.4
5-th percentile31.915
Q136
median38.4
Q341
95-th percentile44.7
Maximum50.6
Range30.2
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8769019
Coefficient of variation (CV)0.10093648
Kurtosis0.097356444
Mean38.409324
Median Absolute Deviation (MAD)2.6
Skewness-0.12069037
Sum252118.8
Variance15.030369
MonotonicityNot monotonic
2024-05-18T16:46:34.163080image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 136
 
2.0%
38 135
 
2.0%
40 133
 
2.0%
42 121
 
1.8%
39 121
 
1.8%
41 103
 
1.5%
37.5 100
 
1.5%
36 89
 
1.3%
38.5 85
 
1.3%
37.2 84
 
1.2%
Other values (222) 5457
80.3%
(Missing) 231
 
3.4%
ValueCountFrequency (%)
20.4 1
 
< 0.1%
24 1
 
< 0.1%
24.9 3
< 0.1%
25 2
 
< 0.1%
25.1 1
 
< 0.1%
25.7 1
 
< 0.1%
25.8 1
 
< 0.1%
26.4 1
 
< 0.1%
26.5 5
0.1%
26.6 1
 
< 0.1%
ValueCountFrequency (%)
50.6 1
 
< 0.1%
50.5 1
 
< 0.1%
50.3 1
 
< 0.1%
50.2 1
 
< 0.1%
50 2
< 0.1%
49.8 4
0.1%
49.5 1
 
< 0.1%
49.4 1
 
< 0.1%
49.1 1
 
< 0.1%
49 3
< 0.1%

arml
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct166
Distinct (%)2.5%
Missing179
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean36.874607
Minimum24.8
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:34.304552image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum24.8
5-th percentile32.5
Q135
median36.8
Q338.8
95-th percentile41.6
Maximum47
Range22.2
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation2.7816164
Coefficient of variation (CV)0.075434469
Kurtosis-0.18539916
Mean36.874607
Median Absolute Deviation (MAD)1.9
Skewness0.11635287
Sum243962.4
Variance7.7373899
MonotonicityNot monotonic
2024-05-18T16:46:34.425773image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 285
 
4.2%
37 284
 
4.2%
38 264
 
3.9%
35 235
 
3.5%
39 188
 
2.8%
34 188
 
2.8%
40 181
 
2.7%
36.5 139
 
2.0%
35.5 133
 
2.0%
37.5 128
 
1.9%
Other values (156) 4591
67.6%
(Missing) 179
 
2.6%
ValueCountFrequency (%)
24.8 1
 
< 0.1%
27 1
 
< 0.1%
27.5 1
 
< 0.1%
28 1
 
< 0.1%
28.2 1
 
< 0.1%
29.1 1
 
< 0.1%
29.2 1
 
< 0.1%
29.4 1
 
< 0.1%
29.5 5
0.1%
29.6 2
 
< 0.1%
ValueCountFrequency (%)
47 1
 
< 0.1%
46 1
 
< 0.1%
45.6 2
 
< 0.1%
45.5 1
 
< 0.1%
45.2 1
 
< 0.1%
45.1 1
 
< 0.1%
45 5
0.1%
44.7 1
 
< 0.1%
44.6 1
 
< 0.1%
44.5 5
0.1%

armc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct320
Distinct (%)4.8%
Missing188
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean32.485152
Minimum16.8
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:34.562274image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum16.8
5-th percentile24.5
Q128.85
median32.1
Q335.6
95-th percentile41.7
Maximum61
Range44.2
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation5.2976599
Coefficient of variation (CV)0.16307942
Kurtosis0.77123379
Mean32.485152
Median Absolute Deviation (MAD)3.4
Skewness0.49894453
Sum214629.4
Variance28.0652
MonotonicityNot monotonic
2024-05-18T16:46:34.688118image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 104
 
1.5%
33 87
 
1.3%
29 85
 
1.3%
30 83
 
1.2%
34 80
 
1.2%
28 79
 
1.2%
31 77
 
1.1%
31.2 74
 
1.1%
32.5 70
 
1.0%
29.5 69
 
1.0%
Other values (310) 5799
85.3%
(Missing) 188
 
2.8%
ValueCountFrequency (%)
16.8 1
< 0.1%
17.6 1
< 0.1%
17.9 1
< 0.1%
18.5 1
< 0.1%
18.9 1
< 0.1%
19 1
< 0.1%
19.1 1
< 0.1%
19.3 2
< 0.1%
19.4 1
< 0.1%
19.5 1
< 0.1%
ValueCountFrequency (%)
61 1
< 0.1%
58.9 1
< 0.1%
58.5 1
< 0.1%
58.3 1
< 0.1%
56 1
< 0.1%
55.3 1
< 0.1%
54.9 2
< 0.1%
54.2 2
< 0.1%
54.1 1
< 0.1%
53.5 1
< 0.1%

waist
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct820
Distinct (%)12.5%
Missing239
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean96.254149
Minimum52
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:34.822703image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile70.8
Q183.5
median95.3
Q3106.9
95-th percentile126.5
Maximum179
Range127
Interquartile range (IQR)23.4

Descriptive statistics

Standard deviation17.059193
Coefficient of variation (CV)0.17723073
Kurtosis0.30293041
Mean96.254149
Median Absolute Deviation (MAD)11.7
Skewness0.4937178
Sum631042.2
Variance291.01607
MonotonicityNot monotonic
2024-05-18T16:46:34.962689image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 34
 
0.5%
103 31
 
0.5%
97.5 30
 
0.4%
96 30
 
0.4%
92 29
 
0.4%
108 29
 
0.4%
87.5 28
 
0.4%
101 28
 
0.4%
88 27
 
0.4%
96.2 27
 
0.4%
Other values (810) 6263
92.2%
(Missing) 239
 
3.5%
ValueCountFrequency (%)
52 1
< 0.1%
54.6 1
< 0.1%
56.6 1
< 0.1%
58.1 1
< 0.1%
58.4 1
< 0.1%
58.6 1
< 0.1%
58.8 1
< 0.1%
59 1
< 0.1%
59.4 1
< 0.1%
59.6 1
< 0.1%
ValueCountFrequency (%)
179 1
< 0.1%
168.7 1
< 0.1%
167 1
< 0.1%
165 1
< 0.1%
163.5 1
< 0.1%
162.7 1
< 0.1%
162.2 1
< 0.1%
160.6 1
< 0.1%
160 1
< 0.1%
157.2 1
< 0.1%

tri
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct354
Distinct (%)5.6%
Missing481
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean18.787726
Minimum2.6
Maximum41.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:35.105439image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile7
Q112
median17.9
Q325
95-th percentile33.8
Maximum41.1
Range38.5
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3193933
Coefficient of variation (CV)0.44281002
Kurtosis-0.72992282
Mean18.787726
Median Absolute Deviation (MAD)6.5
Skewness0.38330764
Sum118625.7
Variance69.212305
MonotonicityNot monotonic
2024-05-18T16:46:35.245672image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 78
 
1.1%
22 76
 
1.1%
13 75
 
1.1%
25 74
 
1.1%
14 73
 
1.1%
17 71
 
1.0%
15 71
 
1.0%
18 70
 
1.0%
16 69
 
1.0%
11 67
 
1.0%
Other values (344) 5590
82.3%
(Missing) 481
 
7.1%
ValueCountFrequency (%)
2.6 1
 
< 0.1%
3.1 1
 
< 0.1%
3.2 1
 
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
< 0.1%
3.6 2
 
< 0.1%
3.8 1
 
< 0.1%
4 8
0.1%
4.2 5
0.1%
ValueCountFrequency (%)
41.1 1
 
< 0.1%
40.6 3
< 0.1%
40.2 1
 
< 0.1%
40 4
0.1%
39.8 2
 
< 0.1%
39.6 3
< 0.1%
39.4 3
< 0.1%
39.2 3
< 0.1%
39.1 1
 
< 0.1%
39 7
0.1%

sub
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct343
Distinct (%)5.9%
Missing971
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean19.961556
Minimum3.8
Maximum40.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:35.417549image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile7.7
Q113
median19.4
Q326.2
95-th percentile35
Maximum40.4
Range36.6
Interquartile range (IQR)13.2

Descriptive statistics

Standard deviation8.3690827
Coefficient of variation (CV)0.41926005
Kurtosis-0.7758856
Mean19.961556
Median Absolute Deviation (MAD)6.6
Skewness0.28352396
Sum116256.1
Variance70.041546
MonotonicityNot monotonic
2024-05-18T16:46:35.558027image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 79
 
1.2%
20 67
 
1.0%
24 66
 
1.0%
13 63
 
0.9%
17 61
 
0.9%
25 60
 
0.9%
9 60
 
0.9%
18 57
 
0.8%
23 57
 
0.8%
14 57
 
0.8%
Other values (333) 5197
76.5%
(Missing) 971
 
14.3%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
4 2
 
< 0.1%
4.2 1
 
< 0.1%
4.4 1
 
< 0.1%
4.5 1
 
< 0.1%
4.6 1
 
< 0.1%
4.8 7
0.1%
4.9 1
 
< 0.1%
5 5
0.1%
5.1 2
 
< 0.1%
ValueCountFrequency (%)
40.4 2
 
< 0.1%
40.3 1
 
< 0.1%
40.2 2
 
< 0.1%
40.1 1
 
< 0.1%
40 10
0.1%
39.8 7
0.1%
39.5 2
 
< 0.1%
39.4 1
 
< 0.1%
39.2 11
0.2%
39 11
0.2%

gh
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6765857
Minimum4
Maximum16.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:35.711323image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.8
Q15.2
median5.5
Q35.8
95-th percentile7.2
Maximum16.4
Range12.4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.96469964
Coefficient of variation (CV)0.16994364
Kurtosis22.813849
Mean5.6765857
Median Absolute Deviation (MAD)0.3
Skewness3.9571124
Sum38572.4
Variance0.93064539
MonotonicityNot monotonic
2024-05-18T16:46:35.850163image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.4 670
 
9.9%
5.5 621
 
9.1%
5.3 612
 
9.0%
5.2 596
 
8.8%
5.6 532
 
7.8%
5.7 454
 
6.7%
5.1 430
 
6.3%
5.8 387
 
5.7%
5 327
 
4.8%
5.9 284
 
4.2%
Other values (89) 1882
27.7%
ValueCountFrequency (%)
4 3
 
< 0.1%
4.1 5
 
0.1%
4.2 2
 
< 0.1%
4.3 13
 
0.2%
4.4 17
 
0.3%
4.5 34
 
0.5%
4.6 48
 
0.7%
4.7 86
 
1.3%
4.8 139
2.0%
4.9 245
3.6%
ValueCountFrequency (%)
16.4 1
< 0.1%
15.5 1
< 0.1%
14.5 1
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
13.9 1
< 0.1%
13.6 1
< 0.1%
13.5 1
< 0.1%
13.4 2
< 0.1%
13.2 1
< 0.1%

albumin
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)0.4%
Missing89
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean4.2736206
Minimum2.5
Maximum5.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:35.981129image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.7
Q14.1
median4.3
Q34.5
95-th percentile4.8
Maximum5.3
Range2.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.32654463
Coefficient of variation (CV)0.076409362
Kurtosis0.66827269
Mean4.2736206
Median Absolute Deviation (MAD)0.2
Skewness-0.26895302
Sum28658.9
Variance0.10663139
MonotonicityNot monotonic
2024-05-18T16:46:36.079893image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.3 826
12.2%
4.4 824
12.1%
4.2 810
11.9%
4.1 697
10.3%
4.5 679
10.0%
4 535
7.9%
4.6 515
7.6%
3.9 406
6.0%
4.7 353
5.2%
3.8 274
 
4.0%
Other values (18) 787
11.6%
ValueCountFrequency (%)
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
2.7 4
 
0.1%
2.8 1
 
< 0.1%
3 3
 
< 0.1%
3.1 5
 
0.1%
3.2 10
 
0.1%
3.3 10
 
0.1%
3.4 25
0.4%
3.5 56
0.8%
ValueCountFrequency (%)
5.3 6
 
0.1%
5.2 13
 
0.2%
5.1 23
 
0.3%
5 62
 
0.9%
4.9 110
 
1.6%
4.8 216
 
3.2%
4.7 353
5.2%
4.6 515
7.6%
4.5 679
10.0%
4.4 824
12.1%

bun
Real number (ℝ)

MISSING 

Distinct59
Distinct (%)0.9%
Missing89
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean12.917686
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:36.213730image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median12
Q315
95-th percentile22
Maximum90
Range89
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.7175706
Coefficient of variation (CV)0.44261571
Kurtosis18.348908
Mean12.917686
Median Absolute Deviation (MAD)3
Skewness2.8376185
Sum86626
Variance32.690613
MonotonicityNot monotonic
2024-05-18T16:46:36.634183image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 686
10.1%
11 674
9.9%
12 608
 
8.9%
9 606
 
8.9%
13 554
 
8.2%
14 493
 
7.3%
8 480
 
7.1%
15 395
 
5.8%
16 327
 
4.8%
7 308
 
4.5%
Other values (49) 1575
23.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 4
 
0.1%
3 16
 
0.2%
4 32
 
0.5%
5 100
 
1.5%
6 199
 
2.9%
7 308
4.5%
8 480
7.1%
9 606
8.9%
10 686
10.1%
ValueCountFrequency (%)
90 1
 
< 0.1%
81 1
 
< 0.1%
63 2
< 0.1%
61 1
 
< 0.1%
59 1
 
< 0.1%
56 1
 
< 0.1%
55 3
< 0.1%
54 2
< 0.1%
53 1
 
< 0.1%
51 1
 
< 0.1%

SCr
Real number (ℝ)

MISSING 

Distinct205
Distinct (%)3.1%
Missing89
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.8786266
Minimum0.14
Maximum15.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.2 KiB
2024-05-18T16:46:36.764720image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile0.55
Q10.7
median0.83
Q30.98
95-th percentile1.28
Maximum15.66
Range15.52
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation0.4452377
Coefficient of variation (CV)0.50674279
Kurtosis314.42887
Mean0.8786266
Median Absolute Deviation (MAD)0.14
Skewness13.860214
Sum5892.07
Variance0.19823661
MonotonicityNot monotonic
2024-05-18T16:46:36.900813image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.77 145
 
2.1%
0.82 142
 
2.1%
0.79 141
 
2.1%
0.74 138
 
2.0%
0.72 138
 
2.0%
0.69 137
 
2.0%
0.73 137
 
2.0%
0.91 137
 
2.0%
0.71 133
 
2.0%
0.76 133
 
2.0%
Other values (195) 5325
78.4%
ValueCountFrequency (%)
0.14 1
 
< 0.1%
0.32 1
 
< 0.1%
0.34 1
 
< 0.1%
0.35 2
 
< 0.1%
0.38 2
 
< 0.1%
0.39 4
0.1%
0.4 3
 
< 0.1%
0.41 8
0.1%
0.42 1
 
< 0.1%
0.43 6
0.1%
ValueCountFrequency (%)
15.66 1
< 0.1%
10.98 1
< 0.1%
9.38 1
< 0.1%
9.13 1
< 0.1%
8.51 1
< 0.1%
7.6 1
< 0.1%
7.31 1
< 0.1%
7.15 1
< 0.1%
6.48 1
< 0.1%
6.34 1
< 0.1%

Interactions

2024-05-18T16:46:28.962392image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:07.164896image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:08.788187image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.560749image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.262544image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:13.922320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:15.586563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:17.222006image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.779543image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.720053image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:22.477141image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:24.094309image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.751523image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.397015image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:29.113024image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:07.300602image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:08.940573image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.716430image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.381786image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.062022image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:15.712695image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:17.335577image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.899084image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.844837image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:22.596543image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:24.204815image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.867583image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.509570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:29.223503image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:07.402663image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:09.051713image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.827886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.481834image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.192207image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:15.830621image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:17.429684image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:19.000673image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.986304image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:22.710574image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:24.324292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.966639image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.611886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:29.345360image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:07.529637image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:09.167813image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.951468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.606390image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.313948image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:15.962832image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:17.554835image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:19.155510image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:21.136595image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:22.834705image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:24.498530image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:26.084658image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.721292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:29.467379image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:07.650843image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:09.465162image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:11.082915image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.723181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.426270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.077548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:17.667868image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:19.272740image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:21.246202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:22.942832image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:24.614570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:26.238170image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.833180image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:29.576079image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:07.764877image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:09.585679image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:11.217450image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.832928image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.535138image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.189825image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:17.813549image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:19.669368image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:21.361328image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.062777image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:24.730293image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:26.347915image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.942665image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:29.708323image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:07.866517image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:09.683626image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:11.343588image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.935556image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.644211image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.293655image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:17.918471image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:19.776126image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:21.467853image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.171175image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:24.839666image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:26.476336image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:28.063203image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:29.819138image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:07.975280image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:09.782771image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:11.454079image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:13.047765image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.765798image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.392752image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.016068image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:19.886122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:21.581259image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.280667image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:24.945524image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:26.596300image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:28.165780image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:30.228296image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:08.090215image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:09.892598image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:11.567490image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:13.165914image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.873816image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.505454image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.121529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.018568image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:21.683301image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.396297image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.060560image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:26.720868image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:28.279481image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:30.352371image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:08.208896image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.006339image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:11.689292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:13.299088image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:14.986764image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.620606image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.233095image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.137563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:21.801512image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.517152image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.178372image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:26.838857image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:28.394428image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:30.476477image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:08.326691image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.133604image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:11.813868image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:13.453793image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:15.104001image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.739645image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.347939image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.261142image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:21.918748image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.635283image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.295612image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:26.952044image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:28.504894image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:30.592665image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:08.441032image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.239265image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:11.920596image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:13.583015image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:15.219126image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.854861image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.451370image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.372582image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:22.041523image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.740705image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.410247image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.067922image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:28.621416image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:30.705086image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:08.559647image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.346434image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.036383image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:13.694324image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:15.331793image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:16.974262image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.560370image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.482968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:22.207579image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.855235image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.524125image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.173894image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:28.736244image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:30.815676image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:08.669491image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:10.447113image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:12.140453image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:13.800651image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:15.450713image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:17.086712image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:18.661326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:20.596214image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:22.347116image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:23.968571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:25.631187image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:27.272627image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-18T16:46:28.847045image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2024-05-18T16:46:37.032537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
agewthtbmilegarmlarmcwaisttrisubghalbuminbunSCrsexreincometxdx
age1.0000.199-0.0490.269-0.2700.1320.1860.4150.1110.2480.506-0.2950.4490.3420.0390.1070.0730.3040.313
wt0.1991.0000.4770.8710.2890.6460.9100.8850.3660.6140.240-0.1920.1160.3120.2690.0900.0280.1690.187
ht-0.0490.4771.0000.0220.7590.7940.2690.191-0.302-0.060-0.0680.2320.0640.4640.6500.1390.0530.0340.031
bmi0.2690.8710.0221.000-0.0750.3120.8960.9170.6120.7630.324-0.3450.1040.1110.0960.0800.0380.2170.235
leg-0.2700.2890.759-0.0751.0000.6330.142-0.005-0.254-0.112-0.2010.234-0.0470.3170.4890.1510.0490.1910.189
arml0.1320.6460.7940.3120.6331.0000.4960.440-0.0920.1580.0900.0570.1400.4710.5370.1340.0290.0700.075
armc0.1860.9100.2690.8960.1420.4961.0000.8380.4770.6660.254-0.2190.1030.2460.1910.0870.0170.1620.182
waist0.4150.8850.1910.917-0.0050.4400.8381.0000.4940.7140.381-0.3210.1760.2010.1050.0820.0400.2460.265
tri0.1110.366-0.3020.612-0.254-0.0920.4770.4941.0000.6600.143-0.335-0.051-0.2650.5180.0570.0310.1210.138
sub0.2480.614-0.0600.763-0.1120.1580.6660.7140.6601.0000.257-0.2690.046-0.0090.1720.0730.0330.1560.184
gh0.5060.240-0.0680.324-0.2010.0900.2540.3810.1430.2571.000-0.2700.2270.1510.0230.0560.0370.6580.590
albumin-0.295-0.1920.232-0.3450.2340.057-0.219-0.321-0.335-0.269-0.2701.000-0.0340.0550.2880.0730.0300.1790.180
bun0.4490.1160.0640.104-0.0470.1400.1030.176-0.0510.0460.227-0.0341.0000.4260.1440.0750.0320.2070.197
SCr0.3420.3120.4640.1110.3170.4710.2460.201-0.265-0.0090.1510.0550.4261.0000.0240.0290.0250.1100.110
sex0.0390.2690.6500.0960.4890.5370.1910.1050.5180.1720.0230.2880.1440.0241.0000.0070.0600.0000.000
re0.1070.0900.1390.0800.1510.1340.0870.0820.0570.0730.0560.0730.0750.0290.0071.0000.1130.0510.033
income0.0730.0280.0530.0380.0490.0290.0170.0400.0310.0330.0370.0300.0320.0250.0600.1131.0000.0680.071
tx0.3040.1690.0340.2170.1910.0700.1620.2460.1210.1560.6580.1790.2070.1100.0000.0510.0681.0000.780
dx0.3130.1870.0310.2350.1890.0750.1820.2650.1380.1840.5900.1800.1970.1100.0000.0330.0710.7801.000

Missing values

2024-05-18T16:46:31.046276image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T16:46:31.384501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-18T16:46:31.659452image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

sexagereincometxdxwthtbmilegarmlarmcwaisttrisubghalbuminbunSCr
0male34.166667Non-Hispanic White[25000,35000)0087.4164.732.2241.540.036.4100.416.424.95.24.86.00.94
1male16.833333Non-Hispanic Black[45000,55000)0072.3181.322.0042.039.526.674.710.210.55.74.69.00.89
2female60.166667Non-Hispanic Black[10000,15000)11116.8166.042.3935.339.042.2118.229.635.66.03.910.01.11
3male26.083333Mexican American[25000,35000)0097.6173.032.6141.738.737.0103.719.023.25.14.28.00.80
4female49.666667Non-Hispanic White[35000,45000)0086.7168.430.5737.536.133.3107.830.328.05.34.313.00.79
5male80.000000Non-Hispanic White[15000,20000)0179.1174.326.0442.840.030.291.18.615.25.44.316.00.83
6male80.000000Non-Hispanic White[15000,20000)1189.6180.127.6243.041.733.3113.719.426.26.84.316.00.90
7male17.416667Other Hispanic[10000,15000)0074.7169.625.9739.838.133.486.012.415.05.14.711.01.00
8male13.000000Non-Hispanic Black[75000,100000)0040.6156.416.6039.233.423.063.613.87.65.64.310.00.46
9female43.000000Non-Hispanic Black[35000,45000)11107.7164.339.9032.736.539.6129.827.0NaN11.03.616.02.54
sexagereincometxdxwthtbmilegarmlarmcwaisttrisubghalbuminbunSCr
6785male43.666667Non-Hispanic White[25000,35000)0091.2177.928.8242.238.436.3105.415.619.25.63.814.01.08
6786female16.333333Other Race Including Multi-Racial>= 1000000067.9165.224.8835.436.029.978.919.020.85.54.212.00.79
6787female12.666667Non-Hispanic Black[25000,35000)0075.7159.329.8340.436.034.095.631.029.85.93.85.00.66
6788male52.250000Non-Hispanic Black[15000,20000)00143.6179.044.8239.240.247.4143.534.026.45.64.214.01.01
6789male24.333333Non-Hispanic Black>= 1000000075.1171.725.4740.239.532.680.610.09.25.84.515.01.12
6790male33.000000Mexican American[35000,45000)0094.3163.535.2834.434.735.5112.320.2NaN5.44.110.00.97
6791female48.916667Non-Hispanic White[0,5000)0187.1156.935.3833.934.537.099.428.625.45.54.17.00.89
6792male27.500000Other Hispanic[35000,45000)0057.0164.321.1235.333.729.673.24.26.85.64.511.00.94
6793male75.750000Non-Hispanic Black[10000,15000)0075.1162.728.3738.636.831.2104.019.821.15.44.019.01.34
6794female63.583333Other HispanicNaN1171.3157.328.8231.433.030.9102.120.119.76.74.315.00.63